Low-rank matrix recovery from quantized and erroneous measurements: Accuracy-preserved data privatization in power grids

Gao, Pengzhi; Ren, Wei; Wang, Meng; Chow, Joe H.

doi:10.1109/acssc.2016.7869063

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Cited by 9 publications

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“…For example, in the Netflix problem where the ratings from the users take integer values between 1 and 5. Classical matrix completion treating the values as continuous-valued yields good results [10], however, performance improvement can be achieved when the observations are treated as quantized [11]- [15].…”

Section: Introductionmentioning

confidence: 99%

Matrix Completion from Quantized Samples via Generalized Sparse Bayesian Learning

Zhu,

Liu,

Zhang

et al. 2019

Preprint

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The recovery of a low rank matrix from a subset of noisy low-precision quantized samples arises in several applications such as collaborative filtering, intelligent recommendation and millimeter wave channel estimation with few bit ADCs. In this paper, a generalized sparse Bayesian learning (Gr-SBL) combining expectation propagation (EP) is proposed to solve the matrix completion (MC), which is termed as MC-Gr-SBL. The MC-Gr-SBL automatically estimates the rank, the factors and their covariance matrices, and the noise variance. In addition, MC-Gr-SBL is proposed to solve the two dimensional line spectral estimation problem by incorporating the MUSIC algorithm. Finally, substantial numerical experiments are conducted to verify the effectiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%